lmList: Fit List of lm Objects with a Common Model

Description

Fit a list of lm objects with a common model for different subgroups of the data.

Usage

lmList(formula, data, family, subset, weights, na.action,
       offset, pool = TRUE, ...)

Arguments

formula

a linear formula object of the form y ~ x1+...+xn | g. In the formula object, y represents the response, x1,...,xn the covariates, and g the grouping factor specifying the partitioning of the data according to which different lm fits should be performed.

family

an optional family specification for a generalized linear model.

pool

logical scalar, should the variance estimate pool the residual sums of squares

...

additional, optional arguments to be passed to the model function or family evaluation.

data

an optional data frame containing the variables named in formula. By default the variables are taken from the environment from which lmer is called. See Details.

subset

an optional expression indicating the subset of the rows of data that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.

weights

an optional vector of ‘prior weights’ to be used in the fitting process. Should be NULL or a numeric vector.

na.action

a function that indicates what should happen when the data contain NAs. The default action (na.omit, inherited from the ‘factory fresh’ value of getOption("na.action")) strips any observations with any missing values in any variables.

offset

this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases. One or more offset terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See model.offset.

Value

an object of class '>lmList4 (see there, notably for the methods defined).

Details

While data is optional, the package authors strongly recommend its use, especially when later applying methods such as update and drop1 to the fitted model (such methods are not guaranteed to work properly if data is omitted). If data is omitted, variables will be taken from the environment of formula (if specified as a formula) or from the parent frame (if specified as a character vector).

Examples

Run this code

# NOT RUN {
fm.plm  <- lmList(Reaction ~ Days | Subject, sleepstudy)
coef(fm.plm)
fm.2  <- update(fm.plm, pool = FALSE)
## coefficients are the same, "pooled or unpooled":
stopifnot( all.equal(coef(fm.2), coef(fm.plm)) )

(ci <- confint(fm.plm)) # print and rather *see* :
plot(ci)                # how widely they vary for the individuals
# }